Big Data Modeling and Analysis for Power Transmission Equipment: A Novel Random Matrix Theoretical Approach

This paper explores a novel idea for power equipment monitoring and finds that random matrix theory is suitable for modeling the massive data sets in this situation. Big data analytics are mined from those data. We extract the statistical correlation between key states and those parameters. In particular, the (empirical) eigenvalue spectrum distribution and the (theoretical) single ring law are derived from large-dimensional random matrices whose entries are modeled as time series. The radii of the single ring law are used as statistical analytics to characterize the measured data. The evaluation of key state and anomaly detection are accomplished through the comparison of those statistical analytics.